Full Stack Data Science, Machine Learning & Generative AI Program Demo

Full Stack Data Science, Machine Learning & Generative AI Program

(Master Python, Machine Learning, Deep Learning, Generative AI, LLMs, RAG & AI Agents with Real-Time Projects)

This comprehensive program provides hands-on training in Python programming, statistics, data analysis, data visualization, SQL, machine learning, deep learning, NLP, Generative AI, Large Language Models (LLMs), prompt engineering, RAG, AI agents, and real-world AI application development.

Learners will gain practical expertise in building end-to-end data science and machine learning solutions, including data preprocessing, feature engineering, exploratory data analysis, model development, model evaluation, deployment, and intelligent AI-powered applications.

The program includes real-time implementation of machine learning and Generative AI projects, helping students develop practical experience with LLM applications, RAG pipelines, vector databases, prompt engineering, AI agents, chatbot development, API integration, and production-ready AI workflows.

By the end of this training, learners will have strong expertise in Full Stack Data Science, Machine Learning, and Generative AI development, complete real-world projects for professional portfolios, and the confidence to pursue roles such as Data Scientist, Machine Learning Engineer, AI Engineer, Generative AI Engineer, and Data Analyst across modern technology-driven organizations.

Sample Videos:

Full Stack Data Science, Machine Learning & Generative AI Program  – Live Training – Demo Recording

Full Stack Data Science, Machine Learning & Generative AI Program  – Live Training – Day 1 Recording

 

◆ Foundation

○ Introduction to AI
○ Definition and Scope
○ Introduction to Programming
○ Google Colab Setup
○ Why Python?

◆ Python Basics

○ Data Types & Type Conversion
○ Variables
○ Operators — Arithmetic, Comparison, Logical, Assignment, Membership, Identity, Bitwise
○ If-else & Nested If-else
○ For Loops & While Loops
○ Break, Continue and Pass

◆ Strings

○ Multiline Strings
○ String Patterns
○ Zero Padding
○ F-strings & Performance vs format()
○ String Functions: strip(), split(), join(), capitalize(), lower(), title(), upper(), istitle(), replace()

◆ Lists

○ List Creation & Retrieval
○ Item Replacement
○ List Comprehension & Mutable Concept
○ Nested Lists
○ List Functions: len(), append(), pop(), insert(), remove(), sort(), reverse()
○ Forward & Backward Indexing
○ Forward, Backward & Step Slicing

◆ Sets & Tuples

○ Sets — Creation, union(), intersection(), difference(), add(), remove(), pop()
○ Tuples — Creation & Immutable Concept
○ Tuple Concatenation & Unpacking
○ Tuple Functions: len(), count(), index(), sorted(), zip()

◆ Dictionaries

○ Dictionary Creation
○ Keys & Values Concept
○ Creating CSV from Python Dictionary
○ Dictionary Functions: keys(), values(), items(), get(), pop(), update(), from_dict(), zip(), clear(), map()

◆ Functions

○ Inbuilt vs User Defined Functions
○ Types of Function Arguments
○ Global vs Local Variables
○ Anonymous Function (Lambda)

◆ File Handling

○ Read Mode
○ Write Mode
○ Append Mode

Module 02: Python Libraries — Pandas & NumPy

◆ Pandas Introduction

○ Series — Creating, Empty Series, Series from List/Array/Column
○ Index in Series & Accessing Values
○ Statistical Operations on Series
○ NaN Values
○ Values, index, dtypes, size
○ Python List vs NumPy Array vs Pandas Series

◆ Series Functions

○ head()
○ tail()
○ sum()
○ count()
○ nunique()
○ sort_values()
○ value_counts()

◆ DataFrames

○ Creating DataFrames from Lists, Dictionaries & Arrays
○ Creating DataFrames from CSV, Excel & Other File Formats
○ DataFrame Attributes: shape, size, dtypes, index, columns
○ DataFrame Methods: head(), tail(), info(), describe()
○ Accessing and Modifying Data: loc, iloc
○ Adding and Dropping Columns and Rows
○ Renaming Columns and Index
○ Identifying Missing Data
○ Handling Missing Data: fillna(), dropna()
○ Replacing Values
○ Filtering Data
○ Sorting DataFrames
○ Applying Functions: apply(), map()
○ Grouping Data: groupby()
○ Merging and Joining DataFrames
○ Concatenating DataFrames
○ Setting and Resetting Index
○ MultiIndex (Hierarchical Indexing)

◆ Data Analysis with Pandas

○ Mathematical and Statistical Operations
○ Aggregation Methods: sum(), mean(), count(), nunique()
○ Pivot Tables and Cross-tabulations
○ Transpose of a DataFrame
○ Inplace Parameter & Ampersand (&) Logical Operator

◆ NumPy

○ Importance in Data Science
○ Creating NumPy Arrays from Lists and Tuples
○ arange(), linspace(), logspace()
○ zeros(), ones(), full()
○ Random Numbers: random(), rand(), randn(), randint()
○ Array Attributes: shape, size, ndim, dtype, itemsize
○ Array Manipulation, Mathematical Operations, Indexing & Slicing
○ NumPy Functions: add(), subtract(), multiply(), divide(), arange(), log(), abs(), reshape(), ravel(), flatten()
○ Statistics: mean(), median(), std(), sum(), min(), corrcoef(), cov()
○ List vs Array Performance Comparison
○ Diagonal, Trace & Identity Matrix
○ Multiplicative Inverse, Determinant & Adjoint Matrix
○ Parsing, Adding and Subtracting Matrices

Module 03: Statistics for Data Science

◆ Mathematical Foundation

○ What are Statistics?
○ Measures of Central Tendency — Mean, Median, Mode
○ Measures of Dispersion — Range, Variance, Standard Deviation
○ Numerical Data — Continuous and Discrete
○ Categorical Data — Nominal and Ordinal
○ Descriptive Statistics
○ Inferential Statistics
○ Prescriptive Statistics

◆ Sampling Techniques

○ Population and Types of Sampling Techniques
○ Random Sampling
○ Systematic Sampling
○ Stratified Random Sampling
○ Cluster Sampling

◆ Data Distribution & Relationships

○ Percentiles and Quantiles — 25%, Median, 75%
○ Skewness — Right Skew, Left Skew
○ Normal Distribution
○ Difference between Independent and Dependent Variables
○ Correlation — Positive, Negative, Zero
○ Multicollinearity & Variance Inflation Factor
○ Causation
○ Central Limit Theorem

◆ Data Preprocessing & Statistical Testing

○ Normalization Types — Standard Scaler, Min-Max Scaler
○ Outliers — Z-Score, Box-Plot, IQR
○ Hypothesis Testing
○ Chi-Squared Test
○ Conditional Probability

Module 04: Machine Learning

◆ Machine Learning Fundamentals

○ What is Machine Learning?
○ Traditional Programming vs Machine Learning
○ Types of ML — Supervised, Semi-Supervised, Unsupervised, Reinforcement Learning
○ Classification — Binary, Multi-Class, Multi-Label
○ Regression
○ Instance Based vs Model Based Learning

◆ Machine Learning Pipeline

○ ETL
○ EDA
○ Training
○ Validation
○ Testing
○ Deployment
○ Monitoring

◆ Linear Regression

○ Gradient Descent
○ Simple Linear Regression
○ Linear Regression Assumptions & Best Fit Line
○ Cost Function & Loss Optimization (Least Squares)
○ Gradient Descent Algorithm
○ Evaluation Metrics — MAE, MSE, RMSE, MSLE, R², Adjusted R²
○ Residual (Error) Analysis
○ Homoscedasticity & Heteroscedasticity
○ Multicollinearity & VIF Math
○ Recursive Feature Elimination (RFE)
○ Multiple Linear Regression

◆ Logistic Regression

○ Logistic Regression — Types
○ Why Not Linear for Classification?
○ Logistic Model — Sigmoid Curve & Activation Function
○ Interpretation of Coefficients & Decision Boundary
○ Cost Function of Logistic Regression
○ Gradient Descent in Logistic Regression
○ Confusion Matrix — Accuracy, Precision, Recall, F1-Score
○ ROC Curve and AUC
○ Classification Report in Python

◆ Decision Trees

○ Decision Tree — Introduction
○ Types — Classification & Regression
○ Decision Tree Training Algorithm
○ Entropy, Information Gain, Gini Index
○ Feature Selection & Node Splitting Technique
○ Decision Tree Feature Importance & Model Evaluation
○ Limitations of Decision Tree

◆ Ensemble Learning

○ Bagging (Bootstrap Aggregation)
○ Random Forest — Classification & Regression
○ Advantages of Random Forest over Decision Trees
○ Random Forest Feature Importance & Model Evaluation
○ Gradient Boosting Machine (GBM) — Boosting
○ Bagging vs Boosting
○ GBM Classification & Regression
○ XGBoost — Introduction & Types
○ XGBoost Classification & Regression
○ XGBoost Feature Importance & Advantages

◆ Model Evaluation & Optimization

○ Model Evaluation — ROC Curve & AUC, Benchmarking
○ K Fold Cross Validation
○ Stratified K Fold Cross Validation
○ LOOCV & Hold Out CV
○ Hyperparameter Tuning — GridSearchCV, RandomizedSearchCV
○ HyperOpt — Bayesian Optimization
○ Model Calibration
○ Model Interpretability & Explainability
○ Overfitting and Underfitting

◆ Other Machine Learning Algorithms

○ KNN Algorithm & KNN Imputation
○ Euclidean Distance & Manhattan Distance
○ L1 (Lasso), L2 (Ridge) & Elastic Net Regularization
○ Encoding Techniques — One-Hot, Label, Ordinal Encoder
○ SMOTE
○ Naïve Bayes
○ K-Means Clustering
○ Harmonic Mean

Module 05: Natural Language Processing (NLP)

◆ Text AI

○ Introduction to NLP
○ Tokenization Techniques
○ Tokenization in spaCy
○ Text Cleaning — regex, lowercasing, punctuation removal
○ Part-of-Speech (POS) Tagging
○ Stemming & Lemmatization
○ Stop Words

◆ Text Representations

○ Introduction to Text Representations
○ Label and One-Hot Encoding
○ Bag of Words
○ TF-IDF
○ N-Grams Based Text Representations
○ Word Embeddings
○ Word2Vec — Continuous Bag of Words (CBOW), Skip Gram
○ Advanced Embeddings — GloVe, ELMo & Transformer Based Embeddings

◆ NLP Applications

○ Use Case — Sentiment Classification with Embedding & ANNs
○ Cosine Similarity
○ N-grams

Module 06: Deep Learning

◆ Neural Networks

○ Introduction to Neural Networks
○ Activation Functions — Sigmoid, Tanh, ReLU and Variants, Softmax
○ ANN and Perceptron

◆ RNN — Recurrent Neural Networks

○ Concept and Need for Sequential/Time-Series Data
○ Comparison with Fully Connected Networks
○ RNN Architecture — Input, Hidden, and Output Layers
○ Hidden State and Recurrence Relation
○ Unfolding Through Time
○ Types of RNN
○ Challenges in RNN

◆ Federated Learning

○ Introduction to Federated Learning
○ Applications and Use Cases

◆ LSTM

○ Concept and Motivation — Overcoming Vanishing Gradient
○ Memory Cells and Gates Introduction
○ LSTM Architecture — Cell State and Long-Term Memory
○ Forget Gate, Input Gate, Output Gate
○ Updating Hidden and Cell States
○ Working of LSTM — Step-by-Step Mathematical Formulation
○ Intuition Behind Gate Operations
○ LSTM Implementation in Python (Keras/TensorFlow)
○ Comparison: RNN vs LSTM vs GRU
○ Use Case — Sentiment Analysis/Text Classification with LSTM

◆ Advanced Deep Learning

○ Transfer Learning
○ Autoencoders — Encoder-Decoder Concept
○ Encoder-Decoder Implementation with TensorFlow
○ Variational Autoencoders (VAE)
○ GAN (Generative Adversarial Networks) — Basic Introduction

Module 07: Generative AI

◆ Frontier AI

○ Transformer Architecture — Understanding in Depth
○ Self Attention & Multi-Headed Attention
○ Positional Encoding
○ Transformer Implementation with TensorFlow

◆ Large Language Models

○ Machine Translation — English to German Translation Model
○ Large Language Models (LLMs)
○ Gemma, LLaMA, Mistral & Other Open-Source LLMs via Hugging Face
○ Practical Applications with Open-Source LLMs for Real-World Use Cases

◆ Retrieval-Augmented Generation

○ Introduction to Retrieval-Augmented Generation (RAG)
○ Introduction to LangChain
○ LangGraph
○ LlamaIndex
○ LangSmith

◆ Prompt Engineering

○ Principles of Prompt Design
○ Few-Shot Prompting
○ Zero-Shot Prompting
○ Chain-of-Thought Prompting
○ Advanced Prompting Techniques for Optimal AI Responses

◆ AI Agents

○ Building Intelligent Systems
○ Exploring Future AI Developments
○ AI Agents & Real-World Applications

FAQ’s –Full Stack Data Science, Machine Learning & Generative AI Program

1. What is this course about?
This course covers Data Science, Machine Learning, Deep Learning, Generative AI, LLMs, RAG, AI Agents, and real-time AI projects with practical implementation.

2. Who can enroll in this course?
Students, freshers, data analysts, software developers, working professionals, and anyone interested in Data Science, Machine Learning, and Generative AI can enroll.

3. Do I need prior Data Science or Machine Learning knowledge?
No, prior knowledge is not mandatory. The course covers Python, Data Science, and Machine Learning fundamentals from beginner to advanced levels.

4. Will I work on real-time projects?
Yes, students will work on real-world Data Science, Machine Learning, and Generative AI projects with practical implementation.

5. What technologies will I learn?
You will learn Python, SQL, Statistics, NumPy, Pandas, Data Visualization, Machine Learning, Deep Learning, NLP, Generative AI, LLMs, RAG, AI Agents, LangChain, APIs, and Vector Databases.

6. Will I get hands-on practice?
Yes, the course includes practical coding sessions, assignments, case studies, model building, AI application development, and real-time project implementation.

7. Will Generative AI and LLMs be covered?
Yes, you will learn Generative AI, LLM fundamentals, Prompt Engineering, embeddings, RAG pipelines, vector databases, and LLM-based application development.

8. Will I learn how to build AI Agents?
Yes, the program covers AI Agent concepts, agent workflows, tool integration, LLM-based agents, and practical AI Agent development.

9. Does the course include interview preparation?
Yes, the course includes interview questions, coding exercises, project discussions, and preparation for Data Science, Machine Learning, and Generative AI roles.

10. What career opportunities can I pursue after completing the program?
You can prepare for roles such as Data Scientist, Machine Learning Engineer, AI Engineer, Generative AI Engineer, NLP Engineer, Data Analyst, and AI/ML Developer.

How can I enroll for this course?

 

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Live Sessions Price:

For LIVE sessions – Offer price after discount is 350 USD 250 USD 159 USD Or 35000NR25000 INR 13,900 Rupees